Description Usage Arguments Examples

Draw a graph of the relative cost of complete factorial, fractional factorial, and unbalanced reduced factorial designs, as presented by Collins, Dziak and Li (2009; https://www.ncbi.nlm.nih.gov/pubmed/19719358). For purposes of illustration, a normally distributed response variable, dichotomous factors, and negligible interactions are assumed in this function.

1 2 3 | ```
RelativeCosts1(number_of_factors, desired_fract_resolution = 4,
min_target_d_per_factor = 0.2, condition_costlier_than_subject = 1,
max_graph_ratio = 5)
``` |

`number_of_factors` |
The number of factors to be tested. |

`desired_fract_resolution` |
The desired resolution of the fractional factorial experiment to be compared. The default value is set to be 4. |

`min_target_d_per_factor` |
The minimum Cohen's d (standardized difference, i.e., response difference between levels on a given factor, divided by response standard deviation) that is desired to be detected with 80 The default value is set to be 0.2. |

`condition_costlier_than_subject` |
The default value is set to be 1. |

`max_graph_ratio` |
The default value is set to be 5. |

1 2 3 4 5 | ```
RelativeCosts1(number_of_factors = 9,
desired_fract_resolution = 4,
min_target_d_per_factor = .2,
condition_costlier_than_subject=1,
max_graph_ratio = 4)
``` |

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